Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Priority
The applicant’s priority to provisional application 63373380 filed on August 24th, 2022 has been accepted.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9, 11-13, and 15 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because although claim 1 recites a battery sorting mechanism, it is merely the destination of data which is indicated.
Independent claim 1:
With regards to step 1 of the eligibility analysis, claim one recites “a computer-implemented method”, thus claim 1 is eligible.
With regards to step 2A, Prong I, claim 1 recites receiving and transmitting data. Although claim 1, recites a battery sorting mechanism, it is merely the destination of data which is indicated and does not indicate improvement in functionality. MPEP 2106.05 (a)(1).iv details that recording or transmitting data by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution are not sufficient to show an improvement in computer-functionality.
With regards to Step 2B, the claims to not include additional elements that are significant enough to amount to more than data transmission. The recitations of sensors and a battery sorting mechanism are just origin and destination, respectively of the data. , MPEP 2106.05(d)(II) (i) provides that the courts have recognized receiving and transmitting data as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Furthermore, the recitation of a “utilizing a classification machine learning model” also does not amount to more than data transmission. The claim does not provide any details about how the classification machine learning model operates.
Dependent claims 2-9, 11-13, and 15
Dependent claims 2-9, 11-13, and 15 have been fully considered as well. The dependent claims add no inventive material substantially different from the analysis associated with claim 1; rather, they merely elaborate the method of the independent claims with more details, none of which add significant elements that transcend what is essentially a process of data transmission. There is no indication that the combinations of elements in these claims improves the functioning of a computer or improves any other technology.
Information Disclosure Statement
The Information Disclosure Statements filed on June 20th, 2024, January 3rd, 2025, October 15th, 2025 and December 30th 2025 have been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: battery sorting mechanism. Battery sorting mechanism is interpreted as any one of the list of options comprising pneumatic actuators, guides, hoists, cranes, platforms, or alternative means for diverting batteries into respective bins of the sorting bins 108 (P0044).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 6, 9-11, 13-14 and 16-20 is/are rejected under 35 U.S.C. 102(a)(2) as being clearly anticipated by Young et al. (US 11747290), hereafter Young.
With regards to claim 1, Young discloses a computer-implemented method (Abstract, method 110 Fig. 11) comprising: receiving, from a plurality of sensors (chemical sensing device 110, physical sensing device 150), a plurality of signals corresponding to a target battery (105); determining, utilizing a classifier machine learning model (Col. 10 L20-25), a predicted battery classification of the target battery from a plurality of battery classifications based on the plurality of signals (Col. 9, L60-62); and indicating, to a battery sorting mechanism, the predicted battery classification of the target battery (Col. 10, L25-28).
With regards to claim 2, Young discloses all the elements of claim 1 as outlined above. Young further discloses wherein the plurality of sensors comprises two or more of an x-ray scanning array (Col. 6, L38-39), a three-dimensional (3D) scanner, an RGB camera (Col. 7, L27-29), or an infrared camera.
With regards to claim 3, Young discloses all the elements of claim 1 as outlined above. Young further discloses determining material attributes of the target battery based on one or more x-ray attenuation measurements received from an x-ray scanning array for the target battery (Col. 6, L22-34); wherein determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery from the plurality of battery classifications based on the plurality of signals comprises determining the predicted battery classification of the target battery based at least in part on the material attributes (Col. 5, L62-Col. 6 L2).
With regards to claim 6, Young discloses all the elements of claim 1 as outlined above. Young further discloses determining a plurality of printed characters or codes disposed on the target battery based on image data of the target battery received from one or more RGB cameras (Col. 7, L29-37); wherein determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery from the plurality of battery classifications based on the plurality of signals comprises determining the predicted battery classification of the target battery based at least in part on the plurality of printed characters or codes (Col. 7, L31-37).
With regards to claim 8, Young discloses all the elements of claim 1 as outlined above. Young further discloses wherein determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery from the plurality of battery classifications based on the plurality of signals comprises utilizing an object detection neural network to determine the predicted battery classification from one or more images of the target battery (Col. 10, L4-L13).
With regards to claim 9, Young discloses a system (Abstract) comprising: one or more memory devices comprising a classifier machine learning model (Col. 10, L25-28) and a plurality of battery classifications (Col. 9, L47-57); and one or more processors (compute device 120) configured to cause the system to perform operations comprising: determining one or more attributes of a target battery from a plurality of signals from a plurality of sensors (Col. 9, L60-62; Col. 10 L20-25)., the one or more attributes comprising one or more of a dimension, battery chemistry, printed characters, or a form factor of the target battery (Col. 6, L35-51); and determining, utilizing the classifier machine learning model, a predicted battery classification of the target battery from a plurality of battery classifications based on the determined one or more attributes of the target battery (Col. 9, L60-62).
With regards to claim 10, Young discloses all the elements of claim 9 as outlined above. Young further discloses indicating, to a battery sorting mechanism, the predicted battery classification of the target battery; and transferring the target battery, utilizing the battery sorting mechanism, into a bin corresponding to the predicted battery classification of the target battery (Col. 10, L25-28).
With regards to claim 11, Young discloses all the elements of claim 9 as outlined above. Young further discloses wherein: the operations further comprise receiving one or more label images and one or more profile images of the target battery from a RGB camera of the plurality of sensors (Col. 7, L29-38); determining the one or more attributes of the target battery comprises: determining, utilizing optical character recognition, the printed characters of the target battery from the one or more label images (Col. 7, L29-38); and determining the form factor of the target battery from the one or more profile images (Col. 7, L31-37); and determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery is based on the printed characters and the form factor (Col. 7, L8-17).
With regards to claim 13, Young discloses all the elements of claim 9 as outlined above. Young further discloses receiving, from the plurality of sensors, additional signals corresponding to additional batteries; and determining, utilizing the classifier machine learning model, additional predicted battery classifications of the additional batteries from the plurality of battery classifications based on the additional signals (Col.5 L53-57).
With regards to claim 14, Young discloses all the elements of claim 13 as outlined above. Young further discloses indicating, to a battery sorting mechanism, the predicted battery classification of the target battery and the additional predicted battery classifications of the additional batteries; and individually transferring each battery of the target battery and the additional batteries, utilizing the battery sorting mechanism, into a plurality of bins respectively associated with the plurality of battery classifications (Col. 6, L3-12).
With regards to claim 16, Young discloses a non-transitory computer-readable medium storing executable instructions (Col. 9, L50-54), which when executed by at least one processor (commute device 120), cause the at least one processor to perform operations comprising: receiving, from a plurality of sensors, a plurality of signals corresponding to a target battery (chemical sensing device 110, physical sensing device 150); determining, utilizing a classifier machine learning model (Col. 10 L20-25), a predicted battery classification of the target battery from a plurality of battery classifications based on the plurality of signals (Col. 10, L25-28); and indicating, to a battery sorting mechanism, the predicted battery classification of the target battery (Col. 9, L60-62).
With regards to claim 17, Young discloses all the elements of claim 16 as outlined above. Young further discloses capturing one or more label images utilizing an RGB camera of the plurality of sensors; and identifying, utilizing object character recognition (OCR), a plurality of printed characters or codes from the one or more label images (Col. 7, L29-37).
With regards to claim 18, Young discloses all the elements of claim 17 as outlined above. Young further discloses capturing one or more profile images of the target battery utilizing one or more RGB cameras of the plurality of sensors; and determining, based on the one or more profile images of the target battery, a form factor of the target battery(Col. 7, L8-17).
With regards to claim 19, Young discloses all the elements of claim 18 as outlined above. Young further discloses capturing x-ray attenuation data for the target battery utilizing an x-ray scanning array of the plurality of sensors (Col. 6, L22-34).
With regards to claim 20, Young discloses all the elements of claim 19 as outlined above. Young further discloses wherein determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery based on the plurality of signals comprises utilizing a decision tree to determine the predicted battery classification based on the x-ray attenuation data, the form factor of the target battery, and the plurality of printed characters or codes from the one or more label images (Col. 6, L61-Col. 7, L3).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Young.
With regards to claim 4, Young discloses all the elements of claim 3 as outlined above. Young does not directly disclose capturing a first set of x-ray attenuation measurements utilizing a first x-ray scanning array oriented at a first orientation relative to the target battery; and capturing a second set of x-ray attenuation measurements utilizing a second x-ray scanning array oriented at a second orientation relative to the target battery; wherein determining the material attributes of the target battery based on the one or more x-ray attenuation measurements comprises determining the material attributes based on the first set of x-ray attenuation measurements and the second set of x-ray attenuation measurements.
However, this is a simple duplication of parts and therefore rendered obvious to a person with ordinary skill in the art before the effective filing date of the invention, in order to ensure a more accurate sort (MPEP 2144.04.VI.B).
Examiner’s Comment
No prior art was found to reject claims 5, 7, 12 and 15
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA LYNN BURKMAN whose telephone number is (571)272-5824. The examiner can normally be reached M-Th 7:30am to 6:00pm EST.
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/J.L.B./Examiner, Art Unit 3653
/MICHAEL MCCULLOUGH/Supervisory Patent Examiner, Art Unit 3653